Text Classification
Transformers
PyTorch
English
roberta
roberta-large
topic
news
text-embeddings-inference
Instructions to use dell-research-harvard/topic-obits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dell-research-harvard/topic-obits with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dell-research-harvard/topic-obits")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dell-research-harvard/topic-obits") model = AutoModelForSequenceClassification.from_pretrained("dell-research-harvard/topic-obits", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 706870759158ba65b4e62ad0fb1c7cde80eff17603918437427c943520d1c114
- Size of remote file:
- 329 MB
- SHA256:
- 440b7086c96acdaa62794c32401bbcd3767ca85bcf4a4a6490e1e594380e4d03
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